A Novel Body Sensor Network for Parkinson's Disease Patients Rehabilitation Assessment

Caldara, Michele; Comotti, D.; Galizzi, Michael; Locatelli, Patrick; Re, V.; Alimonti, Dario; Poloni, M.; Rizzetti, Maria Cristina

doi:10.1109/bsn.2014.28

Cited by 23 publications

(10 citation statements)

References 7 publications

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“…The next most common choice was the lower limbs, a choice which yields a high number of gait parameters. Five studies [41], [72], [75]-[77] investigated accelerometers or gyroscopes on the upper limbs and only one study [78] researched movements of the head.…”

Section: Tug Technologiesmentioning

confidence: 99%

Toward Automating Clinical Assessments: A Survey of the Timed Up and Go

Sprint

Cook

Weeks³

2015

IEEE Rev. Biomed. Eng.

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Older adults often suffer from functional impairments that affect their ability to perform everyday tasks. To detect the onset and changes in abilities, healthcare professionals administer standardized assessments. Recently, technology has been utilized to complement these clinical assessments to gain a more objective and detailed view of functionality. In the clinic and at home, technology is able to provide more information about patient performance and reduce subjectivity in outcome measures. The timed up and go (TUG) test is one such assessment recently instrumented with technology in several studies, yielding promising results towards the future of automating clinical assessments. Potential benefits of technological TUG implementations include additional performance parameters, generated reports, and the ability to be self-administered in the home. In this paper, we provide an overview of the TUG test and technologies utilized for TUG instrumentation. We then critically review the technological advancements and follow up with an evaluation of the benefits and limitations of each approach. Finally, we analyze the gaps in the implementations and discuss challenges for future research towards automated, self-administered assessment in the home.

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Section: Tug Technologiesmentioning

confidence: 99%

Toward Automating Clinical Assessments: A Survey of the Timed Up and Go

Sprint

Cook

Weeks³

2015

IEEE Rev. Biomed. Eng.

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“…Inertial sensors, especially integrated wearable micro inertial sensors, have the advantages of small size, high precision, low energy consumption, and low environmental dependence, which can effectively compensate for the lack of visual recognition technology. It has been widely used in various fields such as competitive sports [5][6], rehabilitation treatment [7][8] and somatosensory games [9]. In this paper, we recognize the main ping-pong movements through nine-axis inertial sensors (accelerometers, gyroscopes, and magnetic field sensors) integrated in smart watches, which is universal and convenient and can achieve high recognition accuracy.…”

Section: Introductionmentioning

confidence: 99%

Ping Pong Motion Recognition based on Smart Watch

Fu¹,

Shu²,

Zhang³

2019

Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)

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Smart watches have become one of the most representative devices in wearable devices because of their unique advantages such as integration, portability, reliability, stability, universality and low environmental dependence. At present, it is mainly used for the monitoring of health indicators such as human heart rate. Whole-body inertial sensing devices cannot meet the actual needs of the general public for virtual sports because of high prices and inconvenient wear. In this paper, a single piece smart watch is used to study the recognition of the most common actions in table tennis which is a kind of fast-moving sport and has many fans through an improved convolution neural network model. The final experimental results show that the recognition accuracy reaches 95.46%, which can basically meet the needs of amateurs' motionSports.

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“…The rapidly growing trend of self-monitoring and personalised healthcare led to an abundance of devices aimed at providing the user with simple quantifiable 1 information of activity levels, energy expenditure and sleep patterns [13,14,15,16]. Studies using different constructed automated systems and aiming towards the identification and quantification of movement, under different conditions and for various applications, have also multiplied in the past couple of decades [18,19,20,21,22].…”

Section: Introductionmentioning

confidence: 99%

Activity Recognition Using Complex Network Analysis

Jalloul¹,

Porée²,

Viardot³

et al. 2018

IEEE J. Biomed. Health Inform.

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In this paper, we perform complex network analysis on a connectivity dataset retrieved from a monitoring system in order to classify simple daily activities. The monitoring system is composed of a set of wearable sensing modules positioned on the subject's body and the connectivity data consists of the correlation between each pair of modules. A number of network measures are then computed followed by the application of statistical significance and feature selection methods. These methods were implemented for the purpose of reducing the total number of modules in the monitoring system required to provide accurate activity classification. The obtained results show that an overall accuracy of 84.6% for activity classification is achieved, using a Random Forest (RF) classifier, and when considering a monitoring system composed of only two modules positioned at the Neck and Thigh of the subject's body.

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A Novel Body Sensor Network for Parkinson's Disease Patients Rehabilitation Assessment

Cited by 23 publications

References 7 publications

Toward Automating Clinical Assessments: A Survey of the Timed Up and Go

Toward Automating Clinical Assessments: A Survey of the Timed Up and Go

Ping Pong Motion Recognition based on Smart Watch

Activity Recognition Using Complex Network Analysis

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